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Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance
for probabilistic logic programming (PLP), lifted inference has been applied
up to now only to relational languages outside of logic programming. In this
paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE)
to the problem of computing the probability of queries to probabilistic logic
programs under the distribution semantics. In particular, we extend the Prolog
Factor Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by
the extension to variable elimination proposed by Zhang and Poole for dealing
with convergent variables and heterogeneous factors. Two new operators are
added to GC-FOVE for treating heterogeneous factors. The resulting algorithm,
called LP2 for Lifted Probabilistic Logic Programming, has been implemented
by modifying the PFL implementation of GC-FOVE and tested on three benchmarks
for lifted inference. A comparison with PITA and ProbLog2 shows the potential
of the approach
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